We need a bunch of these…
First jsonlite for processing the colours data pulled from the website
library(jsonlite)
Next, a bunch of data munging packages from the ‘tidyverse’
library(plyr) # rbind.fill is useful for ragged data with missing entries
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr)
library(tibble)
library(stringr)
And finally the R (vector) spatial packages
library(sf)
## Linking to GEOS 3.8.0, GDAL 3.3.1, PROJ 8.1.0
library(tmap)
library(tmaptools) # for geocode_OSM
tmap_mode("view") # for web maps
## tmap mode set to interactive viewing
See the Dulux website for what this is all about
Prior to building this notebook I had a poke around on the website to figure out where the colour details were to be found.
colour_groups <- c("blues", "browns", "greens", "greys", "oranges",
"purples", "reds", "whites-neutrals", "yellows")
base_url <- "https://www.dulux.co.nz/content/duluxnz/home/colour/all-colours.categorycolour.json/all-colours/"
The loop on the next slide
df_colours <- NULL
for (group in colour_groups) {
source <- str_c(base_url, group) # the URL of the colour group JSON
json <- fromJSON(source, flatten = TRUE)
write_json(json, str_c(group, ".json"))
# the next line needed a bit of experimentation...
the_colours <- rbind.fill(json$categoryColours$masterColour.colours)
if (is.null(df_colours)) {
df_colours <- the_colours
} else {
df_colours <- bind_rows(df_colours, the_colours)
}
Sys.sleep(0.5)
}
write.csv(df_colours, "dulux-colours-raw.csv", row.names = FALSE)
df_colours <- read.csv("dulux-colours-raw.csv")
head(df_colours)
## id red green blue lrv baseId name woodType coats
## 1 149253 205 210 206 67 vivid_white Pukaki Quarter None NA
## 2 149254 220 240 242 86 vivid_white Canoe Bay None NA
## 3 149255 226 240 245 87 vivid_white Mt Dobson None NA
## 4 149256 220 230 235 80 vivid_white Raetihi None NA
## 5 149257 217 219 223 74 vivid_white Taiaroa Head None NA
## 6 149258 180 200 219 60 vivid_white Gulf Harbour None NA
There are paint names with modifiers as suffixes to specify different shades of particular colours, and we need to handle this
The modifiers are
paint_modifiers <- c("Half", "Quarter", "Double")
Here’s one way to clean this up (there are others…)
df_colours_tidied <- df_colours %>%
## remove some columns we won't be needing
select(-id, -baseId, -woodType, -coats) %>%
## separate the name components, filling from the left with NAs if <5
separate(name, into = c("p1", "p2", "p3", "p4", "p5"), sep = " ",
remove = FALSE, fill = "left") %>%
## replace any NAs with an empty string
mutate(p1 = str_replace_na(p1, ""),
p2 = str_replace_na(p2, ""),
p3 = str_replace_na(p3, ""),
p4 = str_replace_na(p4, "")) %>%
## if p5 is a paint modifiers, then recompose name
## from p1:p4 else from p1:p5
## similarly keep modifier where it exists
mutate(placename = if_else(p5 %in% paint_modifiers,
str_trim(str_c(p1, p2, p3, p4, sep = " ")),
str_trim(str_c(p1, p2, p3, p4, p5, sep = " "))),
modifier = if_else(p5 %in% paint_modifiers,
p5, "")) %>%
## remove some places that are tricky to deal with later
filter(!placename %in% c("Chatham Islands",
"Passage Rock",
"Auckland Islands",
"Cossack Rock")) %>%
## throw away variables we no longer and reorder
select(name, placename, modifier, red, green, blue)
# save it so we have it for later
write.csv(df_colours_tidied, "dulux-colours.csv", row.names = FALSE)
Add x and y columns to our data for the coordinates—note that we reload from the saved file so as not to keep hitting the Dulux website
df_colours_tidied <- read.csv("dulux-colours.csv")
df_colours_tidied_xy <- df_colours_tidied %>%
mutate(x = 0, y = 0)
tmaptools::geocode_OSMCode on the next slide
x y coordinates as we have space for (due to the modifiers) from the geocoding resultsBest not to re-run this (it takes a good 10 minutes and it’s not good to repeatedly hit the OSM server)
for (placename in unique(df_colours_tidied_xy$placename)) {
address <- str_c(placename, "New Zealand", sep = ", ")
geocode <- geocode_OSM(address, as.data.frame = TRUE, return.first.only = FALSE)
num_geocodes <- nrow(geocode)
matching_rows <- which(df_colours_tidied_xy$placename == placename)
for (i in 1:length(matching_rows)) {
if (!is.null(geocode)) {
if (num_geocodes >= i) {
df_colours_tidied_xy[matching_rows[i], ]$x <- geocode$lon[i]
df_colours_tidied_xy[matching_rows[i], ]$y <- geocode$lat[i]
}
}
}
Sys.sleep(0.5) # so as not to over-tax the geocoder
}
Another tidy up removing anything that didn’t get geocoded
df_colours_tidied_xy <- df_colours_tidied_xy %>%
filter(x != 0 & y != 0)
write.csv(df_colours_tidied_xy, "dulux-colours-xy.csv", row.names = FALSE)
sf datasetdulux_colours <- read.csv("dulux-colours-xy.csv")
dulux_colours_sf <- st_as_sf(dulux_colours,
coords = c("x", "y"), # the columns with the coordinates
crs = 4326) %>% # the project EPSG:4326 for lng-lat
st_transform(2193) %>% # convert to NZTM
## and make an RGB column
mutate(rgb = rgb(red / 255,
green / 255,
blue/ 255))
st_write(dulux_colours_sf, "dulux-colours-pts.gpkg", delete_dsn = TRUE)
## Deleting source `dulux-colours-pts.gpkg' using driver `GPKG'
## Writing layer `dulux-colours-pts' to data source
## `dulux-colours-pts.gpkg' using driver `GPKG'
## Writing 904 features with 7 fields and geometry type Point.
tmap_mode("view")
## tmap mode set to interactive viewing
tm_shape(st_read("nz.gpkg")) + tm_borders() +
tm_shape(dulux_colours_sf) + tm_dots(col = "rgb")
## Reading layer `nz' from data source
## `/home/osullid3/Documents/code/dulux-colours-map/nz.gpkg' using driver `GPKG'
## Simple feature collection with 1 feature and 0 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 1089972 ymin: 4748123 xmax: 2091863 ymax: 6223160
## Projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000
Points aren’t really much fun
Instead, make Voronoi polygons and clip to NZ
dulux_colours_vor <- dulux_colours_sf %>%
st_union() %>%
st_voronoi() %>%
st_cast() %>%
st_as_sf() %>%
st_join(dulux_colours_sf, left = FALSE) %>%
st_intersection(st_read("nz.gpkg"))
st_write(dulux_colours_vor, "dulux-colours-vor.gpkg", delete_dsn = TRUE)
dulux_colours_vor <- st_read("dulux-colours-vor.gpkg")
## Reading layer `dulux-colours-vor' from data source
## `/home/osullid3/Documents/code/dulux-colours-map/dulux-colours-vor.gpkg'
## using driver `GPKG'
## Simple feature collection with 902 features and 7 fields
## Geometry type: MULTIPOLYGON
## Dimension: XY
## Bounding box: xmin: 1089972 ymin: 4748123 xmax: 2091863 ymax: 6223160
## Projected CRS: NZGD2000 / New Zealand Transverse Mercator 2000
tm_shape(dulux_colours_vor) +
tm_polygons(col = "rgb", id = "placename", alpha = 0.75, border.col = "grey", lwd = 0.2)
sf and tmap (for basic geospatial)